Exploratory multivariate analysis by example using R /
Material type: TextSeries: Chapman & Hall/CRC computer science and data analysisPublication details: Boca Raton : CRC Press, c2011Description: xii, 228 p. : illISBN: 9781439835807 (hardback)Subject(s): Multivariate analysis | R (Computer program language) | MATHEMATICS / Probability & Statistics / GeneralDDC classification: 519.535 Summary: "An introduction to exploratory techniques for multivariate data analysis, this book covers the key methodology, including principal components analysis, correspondence analysis, mixed models and multiple factor analysis. The authors take a practical approach, with examples leading the discussion of the methods and lots of graphics to emphasize visualization. They present the concepts in the most intuitive way possible, keeping mathematical content to a minimum or relegating it to the appendices. The book includes examples that use real data from a range of scientific disciplines and implemented using an R package developed by the authors"--Item type | Current library | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
BK | Kannur University Central Library Stack | Stack | 519.535 HUS/E (Browse shelf (Opens below)) | Available | 31714 |
Browsing Kannur University Central Library shelves, Shelving location: Stack, Collection: Stack Close shelf browser (Hides shelf browser)
519.53 SON/B Basic and advanced Bayesian structural equation modeling : with applications in the medical and behavioural sciences | 519.53 TAN/A Applied categorical and count data analysis / | 519.535 AGR/C Categorical data analysis | 519.535 HUS/E Exploratory multivariate analysis by example using R / | 519.535 WES/L Linear mixed models : a practical guide using statistical software | 519.536 CHA/R Regression analysis by example | 519.536 HIL/L Logistic regression models |
"A Chapman & Hall book".
"An introduction to exploratory techniques for multivariate data analysis, this book covers the key methodology, including principal components analysis, correspondence analysis, mixed models and multiple factor analysis. The authors take a practical approach, with examples leading the discussion of the methods and lots of graphics to emphasize visualization. They present the concepts in the most intuitive way possible, keeping mathematical content to a minimum or relegating it to the appendices. The book includes examples that use real data from a range of scientific disciplines and implemented using an R package developed by the authors"--
There are no comments on this title.